{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== 1/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.097 - 0.117\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yzpang\\Desktop\\DEV\\study\\Python\\DeepLearningIntroduction\\common\\multi_layer_net_extend.py:105: RuntimeWarning: overflow encountered in square\n",
      "  weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W ** 2)\n",
      "C:\\Users\\yzpang\\Desktop\\DEV\\study\\Python\\DeepLearningIntroduction\\common\\multi_layer_net_extend.py:105: RuntimeWarning: invalid value encountered in scalar multiply\n",
      "  weight_decay += 0.5 * self.weight_decay_lambda * np.sum(W ** 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:2 | 0.097 - 0.116\n",
      "epoch:3 | 0.097 - 0.117\n",
      "epoch:4 | 0.097 - 0.116\n",
      "epoch:5 | 0.097 - 0.116\n",
      "epoch:6 | 0.097 - 0.117\n",
      "epoch:7 | 0.097 - 0.117\n",
      "epoch:8 | 0.097 - 0.117\n",
      "epoch:9 | 0.097 - 0.116\n",
      "epoch:10 | 0.097 - 0.116\n",
      "epoch:11 | 0.097 - 0.117\n",
      "epoch:12 | 0.097 - 0.117\n",
      "epoch:13 | 0.097 - 0.117\n",
      "epoch:14 | 0.097 - 0.117\n",
      "epoch:15 | 0.097 - 0.116\n",
      "epoch:16 | 0.097 - 0.117\n",
      "epoch:17 | 0.097 - 0.116\n",
      "epoch:18 | 0.097 - 0.117\n",
      "epoch:19 | 0.097 - 0.117\n",
      "=========== 2/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.226 - 0.116\n",
      "epoch:2 | 0.29 - 0.116\n",
      "epoch:3 | 0.314 - 0.116\n",
      "epoch:4 | 0.335 - 0.117\n",
      "epoch:5 | 0.374 - 0.117\n",
      "epoch:6 | 0.388 - 0.117\n",
      "epoch:7 | 0.423 - 0.117\n",
      "epoch:8 | 0.444 - 0.117\n",
      "epoch:9 | 0.453 - 0.117\n",
      "epoch:10 | 0.459 - 0.117\n",
      "epoch:11 | 0.495 - 0.117\n",
      "epoch:12 | 0.511 - 0.117\n",
      "epoch:13 | 0.528 - 0.117\n",
      "epoch:14 | 0.533 - 0.117\n",
      "epoch:15 | 0.549 - 0.117\n",
      "epoch:16 | 0.575 - 0.117\n",
      "epoch:17 | 0.58 - 0.117\n",
      "epoch:18 | 0.593 - 0.116\n",
      "epoch:19 | 0.604 - 0.116\n",
      "=========== 3/16 ============\n",
      "epoch:0 | 0.138 - 0.117\n",
      "epoch:1 | 0.417 - 0.116\n",
      "epoch:2 | 0.527 - 0.117\n",
      "epoch:3 | 0.652 - 0.116\n",
      "epoch:4 | 0.722 - 0.116\n",
      "epoch:5 | 0.745 - 0.116\n",
      "epoch:6 | 0.808 - 0.116\n",
      "epoch:7 | 0.84 - 0.116\n",
      "epoch:8 | 0.878 - 0.116\n",
      "epoch:9 | 0.898 - 0.116\n",
      "epoch:10 | 0.917 - 0.116\n",
      "epoch:11 | 0.931 - 0.116\n",
      "epoch:12 | 0.944 - 0.116\n",
      "epoch:13 | 0.951 - 0.116\n",
      "epoch:14 | 0.957 - 0.116\n",
      "epoch:15 | 0.968 - 0.116\n",
      "epoch:16 | 0.978 - 0.116\n",
      "epoch:17 | 0.977 - 0.116\n",
      "epoch:18 | 0.982 - 0.116\n",
      "epoch:19 | 0.981 - 0.116\n",
      "=========== 4/16 ============\n",
      "epoch:0 | 0.105 - 0.105\n",
      "epoch:1 | 0.259 - 0.116\n",
      "epoch:2 | 0.349 - 0.117\n",
      "epoch:3 | 0.419 - 0.117\n",
      "epoch:4 | 0.501 - 0.117\n",
      "epoch:5 | 0.563 - 0.117\n",
      "epoch:6 | 0.593 - 0.117\n",
      "epoch:7 | 0.635 - 0.117\n",
      "epoch:8 | 0.662 - 0.117\n",
      "epoch:9 | 0.693 - 0.116\n",
      "epoch:10 | 0.716 - 0.117\n",
      "epoch:11 | 0.721 - 0.116\n",
      "epoch:12 | 0.744 - 0.117\n",
      "epoch:13 | 0.761 - 0.117\n",
      "epoch:14 | 0.79 - 0.117\n",
      "epoch:15 | 0.792 - 0.116\n",
      "epoch:16 | 0.801 - 0.116\n",
      "epoch:17 | 0.82 - 0.116\n",
      "epoch:18 | 0.822 - 0.116\n",
      "epoch:19 | 0.829 - 0.116\n",
      "=========== 5/16 ============\n",
      "epoch:0 | 0.074 - 0.087\n",
      "epoch:1 | 0.091 - 0.105\n",
      "epoch:2 | 0.106 - 0.105\n",
      "epoch:3 | 0.126 - 0.116\n",
      "epoch:4 | 0.148 - 0.117\n",
      "epoch:5 | 0.172 - 0.117\n",
      "epoch:6 | 0.201 - 0.117\n",
      "epoch:7 | 0.224 - 0.116\n",
      "epoch:8 | 0.233 - 0.116\n",
      "epoch:9 | 0.256 - 0.116\n",
      "epoch:10 | 0.278 - 0.116\n",
      "epoch:11 | 0.299 - 0.116\n",
      "epoch:12 | 0.323 - 0.116\n",
      "epoch:13 | 0.35 - 0.116\n",
      "epoch:14 | 0.359 - 0.116\n",
      "epoch:15 | 0.388 - 0.117\n",
      "epoch:16 | 0.416 - 0.116\n",
      "epoch:17 | 0.438 - 0.117\n",
      "epoch:18 | 0.448 - 0.116\n",
      "epoch:19 | 0.459 - 0.116\n",
      "=========== 6/16 ============\n",
      "epoch:0 | 0.067 - 0.097\n",
      "epoch:1 | 0.092 - 0.117\n",
      "epoch:2 | 0.123 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.16 - 0.117\n",
      "epoch:5 | 0.154 - 0.116\n",
      "epoch:6 | 0.155 - 0.117\n",
      "epoch:7 | 0.147 - 0.117\n",
      "epoch:8 | 0.118 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.132 - 0.117\n",
      "epoch:11 | 0.14 - 0.117\n",
      "epoch:12 | 0.18 - 0.117\n",
      "epoch:13 | 0.177 - 0.117\n",
      "epoch:14 | 0.183 - 0.117\n",
      "epoch:15 | 0.187 - 0.117\n",
      "epoch:16 | 0.188 - 0.117\n",
      "epoch:17 | 0.15 - 0.117\n",
      "epoch:18 | 0.166 - 0.117\n",
      "epoch:19 | 0.173 - 0.117\n",
      "=========== 7/16 ============\n",
      "epoch:0 | 0.086 - 0.117\n",
      "epoch:1 | 0.116 - 0.116\n",
      "epoch:2 | 0.116 - 0.116\n",
      "epoch:3 | 0.116 - 0.116\n",
      "epoch:4 | 0.116 - 0.116\n",
      "epoch:5 | 0.116 - 0.116\n",
      "epoch:6 | 0.116 - 0.116\n",
      "epoch:7 | 0.164 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.116 - 0.116\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.117 - 0.117\n",
      "epoch:13 | 0.117 - 0.117\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.117 - 0.117\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.116 - 0.116\n",
      "epoch:19 | 0.116 - 0.116\n",
      "=========== 8/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.116 - 0.116\n",
      "epoch:13 | 0.116 - 0.116\n",
      "epoch:14 | 0.116 - 0.116\n",
      "epoch:15 | 0.116 - 0.116\n",
      "epoch:16 | 0.116 - 0.116\n",
      "epoch:17 | 0.116 - 0.116\n",
      "epoch:18 | 0.116 - 0.116\n",
      "epoch:19 | 0.116 - 0.116\n",
      "=========== 9/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.116 - 0.116\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.116 - 0.116\n",
      "epoch:7 | 0.116 - 0.116\n",
      "epoch:8 | 0.116 - 0.116\n",
      "epoch:9 | 0.116 - 0.116\n",
      "epoch:10 | 0.116 - 0.116\n",
      "epoch:11 | 0.116 - 0.116\n",
      "epoch:12 | 0.116 - 0.116\n",
      "epoch:13 | 0.116 - 0.116\n",
      "epoch:14 | 0.116 - 0.116\n",
      "epoch:15 | 0.116 - 0.116\n",
      "epoch:16 | 0.116 - 0.116\n",
      "epoch:17 | 0.116 - 0.116\n",
      "epoch:18 | 0.116 - 0.116\n",
      "epoch:19 | 0.116 - 0.116\n",
      "=========== 10/16 ============\n",
      "epoch:0 | 0.105 - 0.105\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.116 - 0.116\n",
      "epoch:13 | 0.116 - 0.116\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.117 - 0.117\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.117 - 0.117\n",
      "epoch:19 | 0.117 - 0.117\n",
      "=========== 11/16 ============\n",
      "epoch:0 | 0.116 - 0.116\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.117 - 0.117\n",
      "epoch:13 | 0.117 - 0.117\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.117 - 0.117\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.117 - 0.117\n",
      "epoch:19 | 0.117 - 0.117\n",
      "=========== 12/16 ============\n",
      "epoch:0 | 0.1 - 0.117\n",
      "epoch:1 | 0.116 - 0.116\n",
      "epoch:2 | 0.116 - 0.116\n",
      "epoch:3 | 0.116 - 0.116\n",
      "epoch:4 | 0.116 - 0.116\n",
      "epoch:5 | 0.116 - 0.116\n",
      "epoch:6 | 0.116 - 0.116\n",
      "epoch:7 | 0.116 - 0.116\n",
      "epoch:8 | 0.116 - 0.116\n",
      "epoch:9 | 0.116 - 0.116\n",
      "epoch:10 | 0.116 - 0.116\n",
      "epoch:11 | 0.116 - 0.116\n",
      "epoch:12 | 0.116 - 0.116\n",
      "epoch:13 | 0.116 - 0.116\n",
      "epoch:14 | 0.116 - 0.116\n",
      "epoch:15 | 0.116 - 0.116\n",
      "epoch:16 | 0.116 - 0.116\n",
      "epoch:17 | 0.116 - 0.116\n",
      "epoch:18 | 0.116 - 0.116\n",
      "epoch:19 | 0.116 - 0.116\n",
      "=========== 13/16 ============\n",
      "epoch:0 | 0.116 - 0.116\n",
      "epoch:1 | 0.094 - 0.094\n",
      "epoch:2 | 0.116 - 0.116\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.117 - 0.117\n",
      "epoch:13 | 0.117 - 0.117\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.116 - 0.116\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.117 - 0.117\n",
      "epoch:19 | 0.117 - 0.117\n",
      "=========== 14/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.117 - 0.117\n",
      "epoch:13 | 0.117 - 0.117\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.117 - 0.117\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.117 - 0.117\n",
      "epoch:19 | 0.117 - 0.117\n",
      "=========== 15/16 ============\n",
      "epoch:0 | 0.087 - 0.087\n",
      "epoch:1 | 0.105 - 0.105\n",
      "epoch:2 | 0.105 - 0.105\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.117 - 0.117\n",
      "epoch:8 | 0.117 - 0.117\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.117 - 0.117\n",
      "epoch:11 | 0.117 - 0.117\n",
      "epoch:12 | 0.117 - 0.117\n",
      "epoch:13 | 0.117 - 0.117\n",
      "epoch:14 | 0.117 - 0.117\n",
      "epoch:15 | 0.117 - 0.117\n",
      "epoch:16 | 0.117 - 0.117\n",
      "epoch:17 | 0.117 - 0.117\n",
      "epoch:18 | 0.117 - 0.117\n",
      "epoch:19 | 0.117 - 0.117\n",
      "=========== 16/16 ============\n",
      "epoch:0 | 0.117 - 0.117\n",
      "epoch:1 | 0.117 - 0.117\n",
      "epoch:2 | 0.117 - 0.117\n",
      "epoch:3 | 0.117 - 0.117\n",
      "epoch:4 | 0.117 - 0.117\n",
      "epoch:5 | 0.117 - 0.117\n",
      "epoch:6 | 0.117 - 0.117\n",
      "epoch:7 | 0.116 - 0.116\n",
      "epoch:8 | 0.116 - 0.116\n",
      "epoch:9 | 0.117 - 0.117\n",
      "epoch:10 | 0.116 - 0.116\n",
      "epoch:11 | 0.116 - 0.116\n",
      "epoch:12 | 0.116 - 0.116\n",
      "epoch:13 | 0.116 - 0.116\n",
      "epoch:14 | 0.116 - 0.116\n",
      "epoch:15 | 0.116 - 0.116\n",
      "epoch:16 | 0.116 - 0.116\n",
      "epoch:17 | 0.116 - 0.116\n",
      "epoch:18 | 0.116 - 0.116\n",
      "epoch:19 | 0.116 - 0.116\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 16 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch Norm 算法\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from dataset.mnist import load_mnist\n",
    "from common.optimizer import *\n",
    "from common.util import smooth_curve\n",
    "from common.multi_layer_net_extend import MultiLayerNetExtend\n",
    "\n",
    "# 0.读入MNIST数据\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 减少学习数据\n",
    "x_train = x_train[:1000]\n",
    "t_train = t_train[:1000]\n",
    "\n",
    "# 超参数\n",
    "max_epochs = 20     # 训练轮数\n",
    "train_size = x_train.shape[0]   # 训练数据量\n",
    "batch_size = 100        # 单批次数量\n",
    "learning_rate = 0.01\n",
    "\n",
    "def __train(weight_init_std):\n",
    "    # 设置batchNorm层的神经网络\n",
    "    bn_network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100],\n",
    "                                      output_size=10, weight_init_std=weight_init_std, use_batchnorm=True)\n",
    "    # 未设置batchNorm层的神经网络\n",
    "    network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100],\n",
    "                                      output_size=10, weight_init_std=weight_init_std)\n",
    "    # 优化器使用SGD\n",
    "    optimizer = SGD(lr=learning_rate)\n",
    "\n",
    "    # 训练精准度列表\n",
    "    train_acc_list = []\n",
    "    bn_train_acc_list = []\n",
    "\n",
    "    # 每一个epoch的循环次数\n",
    "    iter_per_epoch = max(train_size / batch_size, 1)\n",
    "    # 已执行epoch数量\n",
    "    epoch_cnt = 0\n",
    "\n",
    "    # 2.开始训练\n",
    "    for i in range(100000000000):\n",
    "         # 获取小批量数据\n",
    "        batch_mask = np.random.choice(train_size, batch_size)\n",
    "        x_batch = x_train[batch_mask]\n",
    "        t_batch = t_train[batch_mask]\n",
    "\n",
    "        for _network in (bn_network, network):\n",
    "            # 计算梯度\n",
    "            grads = _network.gradient(x_batch, t_batch)\n",
    "            optimizer.update(_network.params, grads)\n",
    "\n",
    "        if i % iter_per_epoch == 0:\n",
    "            train_acc = network.accuracy(x_train, t_train)\n",
    "            bn_train_acc = bn_network.accuracy(x_train, t_train)\n",
    "            train_acc_list.append(train_acc)\n",
    "            bn_train_acc_list.append(bn_train_acc)\n",
    "\n",
    "            print(\"epoch:\" + str(epoch_cnt) + \" | \" + str(train_acc) + \" - \" + str(bn_train_acc))\n",
    "\n",
    "            epoch_cnt += 1\n",
    "            if epoch_cnt >= max_epochs:\n",
    "                break\n",
    "    return train_acc_list, bn_train_acc_list\n",
    "\n",
    "# 3.绘制图形\n",
    "weight_scale_list = np.logspace(0, -4, num=16)\n",
    "x = np.arange(max_epochs)\n",
    "for i, w in enumerate(weight_scale_list):\n",
    "    print(\"=========== \" + str(i+1) + \"/16 ============\")\n",
    "    train_acc_list, bn_train_acc_list = __train(w)\n",
    "\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    plt.title(\"W:\" + str(w))\n",
    "    if i == 15:\n",
    "        plt.plot(x, bn_train_acc_list, label='Batch Normalization', markevery=2)\n",
    "        plt.plot(x, train_acc_list, linestyle='--', label=\"Normal(without BatchNorm)\", markevery=2)\n",
    "    else:\n",
    "        plt.plot(x, bn_train_acc_list, markevery=2)\n",
    "        plt.plot(x, train_acc_list, linestyle='--', markevery=2)\n",
    "\n",
    "    plt.ylim(0, 1.0)\n",
    "    if i % 4 == 0:\n",
    "        plt.yticks([])\n",
    "    else:\n",
    "        plt.ylabel(\"accuracy\")\n",
    "    if i < 12:\n",
    "        plt.xticks([])\n",
    "    else:\n",
    "        plt.xlabel(\"epochs\")\n",
    "    #plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  }
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